feat: agent platform initial commit

- FastAPI + HTMX UI: dashboard, agents CRUD, chat, audit, tools - SQLite schema: agents, conversations, messages, audit, sessions - Code-agent sync (agents/*.py -> DB on startup) - MCP client with health check - Token auth (Bearer + session) - LiteLLM integration via llm.py - Docker Compose: agent-platform + mcp-tools services - Smoke tests pass: /health 200, /api/agents 200, / 401
2026-07-05 22:18:00 +02:00
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+# Agent Platform
+
+Eine schlanke, business-taugliche Agent-Plattform mit LiteLLM, Pydantic AI und MCP.
+
+## Architektur
+
+```
+┌─────────────────────────────────────────────┐
+│  agent-platform (Port 8000)                 │
+│  - FastAPI + LiteLLM + Pydantic AI          │
+│  - HTMX-UI (kein JS-Build)                  │
+│  - SQLite                                   │
+│  - Audit-Log, Auth                          │
+└──────────────────┬──────────────────────────┘
+                   │ MCP (HTTP)
+                   ▼
+┌─────────────────────────────────────────────┐
+│  mcp-tools (Port 8501, intern)              │
+│  - fastmcp                                  │
+│  - 6 generische Tools                       │
+└─────────────────────────────────────────────┘
+```
+
+## Quickstart
+
+```bash
+# 1. Env-Datei anlegen
+cp .env.example .env
+# .env editieren: LLM_API_KEY und AUTH_TOKEN setzen
+
+# 2. Starten
+docker compose up -d --build
+
+# 3. UI öffnen
+http://localhost:8000
+
+# 4. Ersten Agent anlegen
+# Im UI: Agents → "Neuen Agent erstellen"
+# System-Prompt: "Du bist ein hilfreicher Assistent."
+# Erlaubte Tools: "echo, calculate, get_time"
+```
+
+## API
+
+```bash
+# Health
+curl http://localhost:8000/health
+
+# Agents
+curl -H "Authorization: Bearer $AUTH_TOKEN" http://localhost:8000/api/agents
+
+# Chat
+curl -X POST -H "Authorization: Bearer $AUTH_TOKEN" \
+  -H "Content-Type: application/json" \
+  -d '{"message": "Hallo!"}' \
+  http://localhost:8000/api/chat/general_assistant
+
+# MCP-Tools
+curl http://localhost:8000/api/tools
+```
+
+## Verfügbare MCP-Tools
+
+- `echo(text)` - Echo-Tool
+- `get_time(timezone_name)` - Aktuelle Zeit
+- `calculate(expression)` - Mathe-Ausdruck (sicher)
+- `http_get(url)` - HTTP-Request (SSRF-Schutz)
+- `list_env(prefix)` - Umgebungsvariablen
+- `json_format(data)` - JSON formatieren
+
+## Konfiguration
+
+Alle Env-Vars sind in `.env.example` dokumentiert.
+
+## Entwicklung
+
+```bash
+# Backend lokal starten (ohne Docker)
+cd agent_platform
+pip install -e .
+LLM_API_KEY=sk-xxx uvicorn api:app --reload
+
+# MCP-Server lokal
+cd mcp_tools
+pip install -r requirements.txt
+python server.py
+```
+
+## Ressourcen
+
+- agent-platform: 256 MB RAM, 0.5 CPU
+- mcp-tools: 128 MB RAM, 0.25 CPU
+- SQLite: 5-10 MB